FiNER-ORD (Financial NER)
Figure 1.
Goal
The FiNER-ORD dataset is a high-quality financial Named Entity Recognition (NER) dataset. Its primary goals are:
To address the lack of open-source, manually annotated NER datasets in the financial domain.
To benchmark NER models on financial news articles.
To identify standard entities (PER, LOC, ORG) in a specialized context.
Description
FiNER-ORD consists of financial news stories collected from Webz.io, manually annotated for named entities.
Dataset Composition
Source: Financial news articles from Webz.io.
Selection: 201 manually annotated articles selected from a larger corpus of 47,851 documents.
- Entities:
PER (Person)
ORG (Organization)
LOC (Location)
Modality: Text
Language: English
Annotation Process
The dataset was manually annotated by experts to ensure high quality, serving as a gold-standard benchmark for financial NER.
Example
Below is a representative example of the NER task in FiNER-ORD:
ID |
Text |
Entities |
|---|---|---|
0 |
Elon Musk, CEO of Tesla, announced the new factory in Berlin. |
PER: Elon Musk, ORG: Tesla, LOC: Berlin |
1 |
Apple Inc. shares rose after the report released in Cupertino. |
ORG: Apple Inc., LOC: Cupertino |
Task Description
Named Entity Recognition
Input: A sentence or paragraph from a financial news article.
Output: A sequence of tags indicating the start and type of entities.
Challenge: Distinguishing between financial entities in complex news narratives.
Evaluation Metrics
F1-Score: The primary metric for evaluating NER performance.
Precision: Accuracy of the predicted entities.
Recall: Ability to find all relevant entities.
Why Use FiNER-ORD?
Real-World Data: Based on actual financial news, reflecting real-world usage.
High-Quality Annotations: Manually curated to minimize noise.
Open Benchmark: Freely available for research, fostering community progress.
References
For dataset access and more details, visit the HuggingFace dataset page.